• As part of Emerging Technology team, developed machine learning models to identify cyber-security risks for customers and build automated alerts. This project - ‘Customer Cyber Trust Score (CTS-c’ utilized a variety of real-time and historical data sources including account login, browsing patterns, device and browser types, ISP, interaction data etc. and I proposed and developed several features based on this datasets particularly based on operating system, browser and malware vulnerability. As part of the AI Ops squad, collaborated with other team-members to aggregate the features, collaborate with the production team and subsequently built a holistic risk profile for CommBank customers with real-time insights, forecasting and identifying at-risk customers in terms of cyber security. Utilized various tools like Splunk, Python, Splunk Dashboard, Git, Jira, Confluence etc. • Developed an analytics framework (‘Project Tomlinson’) using Commonwealth Bank’s internal email data to automatically identify key communication pattern within and across the teams and organizational hierarchies. Made a dashboard to visualize and filter communication network on various level and get insights (Python, C#, Jupyter Notebook)

Lead research and development for Incentives products, strategic planning and software engineering.

Roles included: • Collaborated with multiple industry partners and developed scalable and efficient machine learning models that can generate business insights, predictive models and reveal trends. The position was funded by the Australian Government CRC program (Capital Markets CRC and Digital Health CRC). Industry partners included HAMBS, Medibank, CBHS, Dun and Bradstreet etc. Key projects delivered were – a) A software solution that implement machine learning methods to analyse large clinical data and predicts multiple disease risk (SQL, R, C#, Python).b) Developed metrices and an interactive data visualisation tool for identifying fraudulent signals using ASIC data.c) A portfolio optimization tool (R) that can detect potential anomalies in the casemix data and chooses best subset for manual re-evaluation for value maximisation.• Carry on independent research in improving machine learning features through integration of network measures within the data. Collaborated with data science team and industry partners to work out ways for knowledge translation, developed prototypes and proof of concept.

Chronic disease risk prediction using graph theory, social network analysing and administrative healthcare data.

Software and Web Development, Database management, Server maintenance

software development, viz scripting